feat:add embedding and similarity search for knowledge graph (#17)

* feat:add embedding and similarity search for knowledge graph

* Fix lint

Author: beinan

python/pyproject.toml

@@ -41,7 +41,7 @@ requires = ["maturin>=1.4"]
 build-backend = "maturin"
 
 [project.optional-dependencies]
-tests = ["pytest", "pyarrow>=14", "pandas"]
+tests = ["pytest", "pyarrow>=14", "pandas", "ruff"]
 dev = ["ruff", "pyright"]
 llm = ["openai>=1.52.0"]
 lance-storage = ["lance>=0.17.0"]

python/python/knowledge_graph/embeddings.py

@@ -0,0 +1,108 @@
+"""Embedding utilities backed by the OpenAI client."""
+
+from __future__ import annotations
+
+import logging
+import math
+from typing import Any, Mapping, MutableMapping, Sequence
+
+LOGGER = logging.getLogger(__name__)
+
+DEFAULT_EMBEDDING_MODEL = "text-embedding-3-small"
+
+
+class EmbeddingGenerator:
+    """Generate embeddings using OpenAI's embeddings API."""
+
+    def __init__(
+        self,
+        *,
+        model: str = DEFAULT_EMBEDDING_MODEL,
+        client_options: Mapping[str, Any] | None = None,
+    ) -> None:
+        self._model = model
+        self._client_options: MutableMapping[str, Any] = dict(client_options or {})
+        self._client = None
+
+    def embed(self, texts: Sequence[str]) -> list[list[float]]:
+        """Return embeddings for the provided texts."""
+        sanitized = [text for text in texts if text]
+        if not sanitized:
+            return []
+
+        client = self._ensure_client()
+        response = client.embeddings.create(model=self._model, input=sanitized)
+        data = getattr(response, "data", None)
+        if not data:
+            raise RuntimeError("OpenAI embedding response missing 'data' entries.")
+
+        embeddings: list[list[float]] = []
+        for item in data:
+            vector = getattr(item, "embedding", None)
+            if vector is None and isinstance(item, Mapping):
+                vector = item.get("embedding")
+            if vector is None:
+                raise RuntimeError("OpenAI embedding response missing 'embedding'.")
+            try:
+                embeddings.append([float(value) for value in vector])
+            except (TypeError, ValueError) as exc:
+                raise RuntimeError(
+                    "Embedding vector contains non-numeric values."
+                ) from exc
+        return embeddings
+
+    def embed_one(self, text: str) -> list[float] | None:
+        """Return a single embedding for convenience."""
+        vectors = self.embed([text])
+        return vectors[0] if vectors else None
+
+    def _ensure_client(self):
+        if self._client is None:
+            try:
+                from openai import OpenAI  # type: ignore[import-not-found]
+            except ImportError as exc:
+                raise RuntimeError(
+                    "The `openai` package is required for embeddings. "
+                    "Install it or supply a custom client."
+                ) from exc
+
+            sanitized_opts = _sanitize_options(self._client_options)
+            LOGGER.debug(
+                "Initializing OpenAI embeddings client",
+                extra={
+                    "lance_graph": {
+                        "openai_model": self._model,
+                        "openai_options": sanitized_opts,
+                    }
+                },
+            )
+            self._client = OpenAI(**self._client_options)
+        return self._client
+
+
+def cosine_similarity(lhs: Sequence[float], rhs: Sequence[float]) -> float:
+    """Return cosine similarity between two vectors."""
+    if len(lhs) != len(rhs):
+        LOGGER.debug(
+            "Unable to compute cosine similarity due to mismatched lengths: %s vs %s",
+            len(lhs),
+            len(rhs),
+        )
+        return 0.0
+    dot = sum(x * y for x, y in zip(lhs, rhs))
+    lhs_norm = math.sqrt(sum(x * x for x in lhs))
+    rhs_norm = math.sqrt(sum(y * y for y in rhs))
+    if lhs_norm == 0 or rhs_norm == 0:
+        return 0.0
+    return dot / (lhs_norm * rhs_norm)
+
+
+def _sanitize_options(options: Mapping[str, Any]) -> dict[str, Any]:
+    """Strip sensitive values for logging."""
+    sanitized: dict[str, Any] = {}
+    for key, value in options.items():
+        if key.lower() in {"api_key", "api-key", "authorization"}:
+            sanitized[key] = "***"
+        else:
+            sanitized[key] = value
+    return sanitized